The present invention relates generally to machine diagnostics, and more specifically, to a system and method for configuring repair codes for diagnostics of machine malfunctions.
A machine, such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiencesxe2x80x94or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively little pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair.
CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or xe2x80x9cgold standardxe2x80x9d cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach which uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs are grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. Pat. No. 6,343,236, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. Pat. No. 6,415,395, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations.
Further, U.S. Pat. No. 6,336,065, titled, xe2x80x9cA Method and System for Analyzing Fault and Snapshot Operational Parameter Data For Diagnostics of Machine Malfunctionsxe2x80x9d, and assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method that uses snapshot observations of operational parameters from the machine in combination with the fault log data in order to further enhance the predictive accuracy of the diagnostic algorithms used therein. In each of the foregoing approaches, it would be desirable to have accurate and reliable output and/or feedback to the diagnostic tools for machine repairs and/or handling of replaceable components by using repair codes configured to accurately and unambiguously address each respective predicted repair. Thus, it would be desirable to have repair codes configured to precisely and accurately pinpoint to respective components and/or repairs notwithstanding that the machine may have hundreds or even thousands of components, some of them substantially interrelated to one another. It would be further desirable to systematically maintain a database wherein the repair codes are kept substantially up to date notwithstanding deployment of new models and/or configurations either in the diagnostic tools and/or the machine.
Generally speaking, the present invention fulfills the foregoing needs by providing a process for populating a database of repair codes used by respective diagnostic tools to identify repairs of respective machines, the process allows for collecting a list of available repair codes. The process further allows for executing expert analysis upon the collected list so as to determine compatibility of the respective repair codes therein with the diagnostic tools. A customizing step allows for customizing the list of repair codes based upon the executed expert analysis, and a storing step allows for storing the customized list of repair codes in the database of repair codes.
The present invention further fulfills the foregoing needs by providing a system for populating a database of repair codes used by respective diagnostic tools to identify repairs of respective machines. The system includes means for collecting a list of available repair codes. The system further includes means for executing expert analysis upon the collected list so as to determine compatibility of the respective repair codes therein with the diagnostic tools. Customizing means is provided to customize the list of repair codes based upon the executed expert analysis, and storing means allow to store the customized list of repair codes in the database of repair codes.